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人工神经网络在海浪数值预报中的应用
引用本文:齐义泉,张志旭,李志伟,李毓湘,施平.人工神经网络在海浪数值预报中的应用[J].水科学进展,2005,16(1):32-35.
作者姓名:齐义泉  张志旭  李志伟  李毓湘  施平
作者单位:1.中国科学院南海海洋研究所LED重点实验室, 广东, 广州, 510301;
基金项目:国家自然科学基金资助项目(49976006)~~
摘    要:探讨将人工神经网络技术和传统的数值模式相结合,以期得到一个更有效的海浪预报方法.以第3代海浪模式的模拟结果作为输入,浮标观测资料作为输出,采用人工神经网络进行训练,训练的初步结果显示,人工神经网络可以改进海浪数值模式的预报精度,但在波高比较大时,改进的效果并不令人满意.为此,对观测值大于1.5m时的有效波高进行再训练,从而结果有了进一步的改善.研究结果证明人工神经网络技术可以提高海浪数值预报的精度.

关 键 词:人工神经网络    海浪数值模式    有效波高    数值预报
文章编号:1001-6791(2005)01-0032-04
收稿时间:2003-11-19
修稿时间:2003年11月19

Application of artificial neural network to numerical wave prediction
QI Yi-quan,ZHANG Zhi-xu,LI Chi-wei,LI Yok-sheung,SHI Ping.Application of artificial neural network to numerical wave prediction[J].Advances in Water Science,2005,16(1):32-35.
Authors:QI Yi-quan  ZHANG Zhi-xu  LI Chi-wei  LI Yok-sheung  SHI Ping
Institution:1.Led, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China;2.Hong Kong Polytechnic University, Hong Kong, China
Abstract:The objective of this paper is to use an artificial neural network (ANN) model to train the output of a third generation wave model to better forecast the significant wave heights from buoy data. After training, the agreement between the wave model's output and the buoy data generally increases, but there is still significant disagreement when the wave height is at its peak. The significant wave heights bigger than 1.5m are selected to retrain, using the same ANN model, and the resulting improvement in the forecast is obvious since the root mean square error (RMS) between the ANN output and the buoy data decrease from 0.31 m to 0.29 m. The goal of this paper is to investigate the feasibility of using an ANN to improve a wave (model's) numerical wave prediction so as to develop a more accurate wave forecasting system. The results show that an ANN is an useful tool for this purpose.
Keywords:artificial neural network  numerical wave model  significant wave height  numerical prediction
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